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Stats Books (Including R)

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16 Free Data Science Books. ONLINE OPEN-ACCESS TEXTBOOKS. Search form You are here Forecasting: principles and practice Rob J Hyndman George Athana­sopou­los Statistical foundations of machine learning Gianluca Bontempi Souhaib Ben Taieb Electric load forecasting: fundamentals and best practices Tao Hong David A.


Modal logic of strict necessity and possibility Evgeni Latinov Applied biostatistical analysis using R Stephen B. Introduction to Computing : Explorations in Language, Logic, and Machines David Evans. Book: stats done wrong. Mining of Massive Datasets. The book has now been published by Cambridge University Press.

Mining of Massive Datasets

The publisher is offering a 20% discount to anyone who buys the hardcopy Here. By agreement with the publisher, you can still download it free from this page. Cambridge Press does, however, retain copyright on the work, and we expect that you will obtain their permission and acknowledge our authorship if you republish parts or all of it. We are sorry to have to mention this point, but we have evidence that other items we have published on the Web have been appropriated and republished under other names. It is easy to detect such misuse, by the way, as you will learn in Chapter 3. --- Jure Leskovec, Anand Rajaraman (@anand_raj), and Jeff Ullman Download Version 2.1 The following is the second edition of the book, which we expect to be published soon.

There is a revised Chapter 2 that treats map-reduce programming in a manner closer to how it is used in practice, rather than how it was described in the original paper. R for Data Science. One of the best ways to improve your reach as a data scientist is to write functions.

R for Data Science

Functions allow you to automate common tasks. Writing a function has three big advantages over using copy-and-paste: You drastically reduce the chances of making incidental mistakes when you copy and paste.As requirements change, you only need to update code in one place, instead of many.You can give a function an evocative name that makes your code easier to understand. Writing good functions is a lifetime journey.

Even after using R for many years we still learn new techniques and better ways of approaching old problems. As well as practical advice for writing functions, this chapter also gives you some suggestions for how to style your code. When should you write a function? You should consider writing a function whenever you’ve copied and pasted a block of code more than twice (i.e. you now have three copies of the same code). Advanced R Programming. The R Inferno. Ramarro R for Developers. Thesis: practical tools for exploring data and models. Practical tools for exploring data and models This thesis describes three families of tools for exploring data and models.

Thesis: practical tools for exploring data and models

It is organised in roughly the same way that you perform a data analysis. First, you get the data in a form that you can work with. Chapter 2 describes the reshape framework for restructuring data. Second, you plot the data to get a feel for what is going on. Download (1.8 meg, pdf) Buy online (122 pages, $25 + shipping) Watch model visualisation videos R packages Reshaping data with the reshape package: reshape A layered grammar of graphics: ggplot2 Visualisation models: classifly, clusterfly, meifly Seminar Slides (printer friendly) Some Free R Books on CRAN. Digital History Methods in R. An R "meta" book. By Joseph Rickert I am a book person.

An R "meta" book

I collect books on all sorts of subjects that interest me and consequently I have a fairly extensive collection of R books, many of which I find to be of great value. Nevertheless, when I am asked to recommend an R book to someone new to R I am usually flummoxed. R is growing at a fantastic rate, and people coming to R for the first time span I wide range of sophistication. And besides, owning a book is kind of personal. Recently, however, while crawling around CRAN, it occurred to me that there is a tremendous amount of high quality material on a wide range of topics in the Contributed Documentation page that would make a perfect introduction to all sorts of people coming to R.

The content column lists the topics that I think ought to be included in a good introductory probability and statistics textbook. Finally, I don’t mean to imply that the documents in my table are the best assembled in the Contributed Documentation page.